arXiv Artificial Intelligence

Semantic Adapter Routing with Fine-Tuning Task Embeddings

2 hours ago 10.08.2026 07:00 👁 1 views
Semantic Adapter Routing with Fine-Tuning Task Embeddings

Quick summary

arXiv:2606.19079v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters. Given such a library, routing aims to select the most appropriate adapter for a user query. While existing adapter routers typically require access to adapter weights or supervised training, we develop training-free semantic adapter routing methods using task embeddings. In ARIADNE, we reframe adapter selection as a classification problem, where PEFT adapters are represented by task embeddings and an unl

Key takeaways

  • arXiv:2606.19079v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters.
  • Given such a library, routing aims to select the most appropriate adapter for a user query.
  • While existing adapter routers typically require access to adapter weights or supervised training, we develop training-free semantic adapter routing methods using task embeddings.

Why it matters

“Semantic Adapter Routing with Fine-Tuning Task Embeddings” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗